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Five Hot Trends for 2018

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Business outcomes and technology priorities in data and analytics.

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Five Hot Trends for 2018

  1. 1. Jake Freivald Five Hot Trends for 2018 Business outcomes and technology priorities in data and analytics Product Marketing December, 2017
  2. 2. The Promise of 2018 The Internet of Things Takes Off The Enhanced Power of Embedded Analytics Predictions, not “Predictive” Real Artificial Intelligence Data Monetization for a Happy CFO 2
  3. 3. Internet of Things 3 Five Hot Trends for 2018
  4. 4. Internet of Things Smarter devices in the IoT are increasing the need to centralize, contextualize, and manage data to improve decisions and business processes. Manufacturing Sharing smart device data can differentiate commodity products. Government and Smart Cities Sensors can help central planners decide how to best deploy resources and regulate requirements. Logistics and Supply Chain The IoT can bring an aircraft, replacement parts, and a skilled tech to the same place at the same time. Health Care The IoT can provide data that can predict, prevent, and help prosecute fraud, theft, and inefficiencies that affect patient outcomes.
  5. 5. Computing power at the edge drives autonomous action closer to the device location to improve response times and reduce traffic to the central processor Internet of Things In humans, this is called the reflex arc... ...the greatest strength and weakness of which is that it doesn’t involve the brain. Photo credit: bit.ly/2AiDXeX
  6. 6. Internet of Things A dirty little secret Just like “Big Data” is becoming “data” ...the “Internet of Things” is already mostly just the Internet Needs communication, context, integration, mastering, analytics, data discovery, information delivery, reporting, scoring, and presentation 2018 trends to watch: “Cloud to the edge ” More IoT deployments go cloud-based
  7. 7. Embedded BI and Analytics 7 Five Hot Trends for 2018
  8. 8. Embedded BI and Analytics Your brain It’s always with you, and always on. ...and you don’t need to tap into all of it at once. You don’t need to do anything special to interact with it. ...though you may need to focus it sometimes. Why shouldn’t analytics be the same? 8
  9. 9. Embedded BI and Analytics Issues driving 2018 changes Pendulum swing: Centralized to decentralized and back again Embedded is generally not an area for standalone analytical tools Analytics adoption has hit a wall Right information, right time is a mantra, but hasn’t been fulfilled SaaS application adoption
  10. 10. Embedded BI and Analytics Import all the data legacy data warehouse other cloud Physically load – and pay for – any needed data Embedded legacy data warehouse other cloud Use data as needed Swivel-chair analytics
  11. 11. Predictions, not “Predictive” 11 Five Hot Trends for 2018
  12. 12. Predictions, Not “Predictive” The Monty Hall Problem 1. You pick a door (say, #1). 2. Monty shows you another door, empty (say, #2). 3. He offers to let you switch to #3, or stay with #1. What do you choose? First, the answer: Always switch. Second, it doesn’t matter whether you agree with me or not: Every statistician knows that’s the right answer. Third, most ordinary people will go round and round (and round) with this problem. The analytics? Hard. The prediction? Easy. Lesson: Give the prediction.
  13. 13. Predictions, Not “Predictive” Or... go there!Use predictive analytics Weather patterns Typical crime levels per type Concerts and events School days and weekdays Holidays and weekends Paydays Shift / time of day ....
  14. 14. Predictions, Not “Predictive” Market watch Predictive analytics suddenly becomes AI or machine learning (For that matter, lots of things do. More on that in a moment.) Prescriptive analytics goes the same route Market watch Despite the need for predictions, vendors will tout predictive analytics “for the businessperson”
  15. 15. Predictions, Not “Predictive” 2018 areas we’ll see growth in “predictive analytics” (or shrink-wrapped predictions) Healthcare: better treatment outcomes Supply chain management: automated supplier, routing choices Financial services: though with skepticism / throttling Customer relationships: e.g., best-offer optimization
  16. 16. Real Artificial Intelligence 16 Five Hot Trends for 2018
  17. 17. Real Artificial Intelligence What is it? To some extent, who cares?  Self-directing vacuum?  Autonomous farming vehicle? ...okay, fine, some terms  Algorithms  Machine learning ” “Transform nature of workthe and the structure of the workplace
  18. 18. Real Artificial Intelligence What is it? To some extent, who cares?  Self-directing vacuum?  Autonomous farming vehicle? ...okay, fine, some terms  Algorithms  Machine learning ” “highly scoped machine-learning solutions that target a specific task
  19. 19. 19 Real Artificial Intelligence Pattern matching across heterogeneous data sets, e.g.,  Metadata  Data  Analytical objects Specific tasks such as...? Anomaly detection  Repeated data quality issues  Match/merge assistance  False positives or negatives  Identifying patterns slightly above the noise floor for humans to investigate
  20. 20. Real Artificial Intelligence
  21. 21. Real Artificial Intelligence Is there anything new? “Cheap gas”  Storage  Computing power  Bandwidth AI swarms ...and where will we see failures in 2018? “AI helps with unbiased decision-making” “Take humans out of the equation” To do it right Help humans, don’t replace them Create advanced user experiences Sometimes called “augmented intelligence”
  22. 22. Data Monetization for a Happy CFO 22 Five Hot Trends for 2018
  23. 23. Data Monetization Current, saturating McKinsey, 12/17
  24. 24. Data Monetization Areasfor growth McKinsey, 12/17
  25. 25. McKinsey, 12/17 The changes are new and continuing Across industries, most respondents agree that the primary objective of their data-and-analytics activities is to generate new revenue.... Of the 41 percent of respondents whose companies have begun to monetize data, a majority say they began doing so just in the past two years.
  26. 26. Defining Your Data Monetization Strategy Step 1 – Which Information Will Deliver the Most Value? Step 2 – Where is the Data Coming From? Step 3 – Is the Data Ready to Be Monetized? Step 4 – Can All Stakeholders Participate in Data Monetization? Monetizing Data
  27. 27. Data Monetization Our experience: externally facing BI applications will yield more measurable returns Enhance customer “stickiness.” Customers spend more time with you, which gives you more opportunities to interact with them Competitive advantage. It can give you a leg up on competitors as you offer more value-added services Process improvement. Reduce cost and eliminate bottlenecks Increase market share. New data-and-analytics products can open doors you once had a problem opening 27
  28. 28. Data Monetization Possible types of monetization Benchmarking! Offering analytics on top of commodity products Unify your data with external data (e.g., weather, economy) for additional insights Interactive e-statements
  29. 29. Data Monetization Considerations Make sure your house is in order: mastered and suitable quality data Customer-facing applications need high-quality data – they know themselves better than you do Data products often need more than just what you get natively; you might end up reselling data
  30. 30. Recap The Internet of Things Takes Off The Enhanced Power of Embedded Analytics Predictions, not “Predictive” Real Artificial Intelligence Data Monetization for a Happy CFO 30
  31. 31. Questions? 31

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